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PurposeThe purpose of this paper is to contribute to the understanding of the relationship between information sharing and performance of perishable product supply chains (PPSC). Building on transaction cost economics (TCE), organisational information processing theory (OIPT), and contingency theory (CT), this study proposes a theoretical framework to guide future research into information sharing in perishable product supply chains (IS-PPSC).Design/methodology/approachUsing the systematic literature review methodology, 48 peer-reviewed articles are carefully selected, mapped, and assessed. Template analysis is performed to unravel the relationship mechanisms between information sharing and PPSC performance.FindingsThe authors find that the relationship between information sharing and PPSC performance is currently unclear, and there is inconsistency in the positioning of information sharing among constructs and variables in the IS-PPSC literature. This implies a requirement to refine the relationship between information sharing and PPSC performance. The review also revealed that the role of perishable product characteristics has largely been ignored in existing research.Originality/valueThis study applies relevant multiple theoretical perspectives to overcome the ambiguity of the IS-PPSC literature and contributes nine propositions to guide future research. Accordingly, this study contributes to the refined roles of relationship uncertainty, environmental uncertainty, information sharing capabilities, and perishable product characteristics in shaping the relationship between information sharing and PPSC performance.

The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.

The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.

The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.

The Paris Agreement set targets to limit global warming to less than 2°C above the pre-industrial level to significantly reduce the risks and impacts associated with climate change [1]. Globally, the energy supply sector is responsible for 25% of greenhouse gas (GHG) emissions [2]. In addition to ratifying Paris Agreement, the UK government has adopted legally binding 80% emissions reduction target from 1990 levels by 2050 as outlined in Climate Change Act. The decarbonisation of power supply, along with electrification of heat and transport, are highlighted as key elements of this transition by both policy and academic research [3]–[5]. Storage systems, via the multiple services they offer across the electricity supply chain [6] at different operational scales stand to create system-wide benefits, enhanced flexibility and reliability for effective management of the grid [7]. The potential contributions storage systems can make towards minimizing the carbon intensity of UK grid with high levels of renewables is recognised by the government as well [8]. This study aims i) to determine the amount of load shifting that can be achieved by the combination of current renewable energy mainly wind and solar and UK grid level storage, ii) analyse the amount of renewable energy generation and storage (RES) needed to phase out programmable gas power generation during the periods of peak demand and iii) assess their economic and environmental implications. The environmental impacts considered are the life cycle emissions associated with electricity generation from the UK mix and the production, installation and use of batteries. The analysis will be extended to cover the future energy scenarios.